Add of tests repository to play with pygraph. Use of virtualenv

tests/README.md

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+To use the library : 
+$> virtualenv --python=/usr/bin/python3.5 venv
+$> pip install -r requirements.txt
+$> source venv/bin/activate
+... Go use pygraph

tests/opt.py

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+import ot
+import sys
+import pathlib
+sys.path.insert(0, "../")
+
+from pygraph.utils.graphfiles import loadDataset
+from pygraph.ged.costfunctions import ConstantCostFunction
+from pygraph.utils.utils import getSPLengths
+from tqdm import tqdm
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from pygraph.ged.GED import ged
+import scipy
+
+def pad(C, n):
+    C_pad = np.zeros((n, n))
+    C_pad[:C.shape[0], :C.shape[1]] = C
+    return C_pad
+
+if (__name__ == "__main__"):
+    ds_filename = "/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds"
+    dataset, y = loadDataset(ds_filename)
+    cf = ConstantCostFunction(1, 3, 1, 3)
+    N = len(dataset)
+
+    pairs = list()
+    
+    ged_distances = list() #np.zeros((N, N))
+    gw_distances = list() #np.zeros((N, N))
+    for i in tqdm(range(0, N)):
+        for j in tqdm(range(i, N)):
+            G1 = dataset[i]
+            G2 = dataset[j]
+            n = G1.number_of_nodes()
+            m = G2.number_of_nodes()
+            if(n == m):
+                C1 = getSPLengths(G1)
+                C2 = getSPLengths(G2)
+
+                C1 /= C1.max()
+                C2 /= C2.max()
+
+                dim = max(n, m)
+                if(n < m):
+                    C1 = pad(C1, dim)
+                elif (m < n):
+                    C2 = pad(C2, dim)
+
+                p = ot.unif(dim)
+                q = ot.unif(dim)
+
+                gw = ot.gromov_wasserstein(C1, C2, p, q,
+                                           'square_loss', epsilon=5e-3)
+                row_ind, col_ind = linear_sum_assignment(-gw)
+                rho = col_ind
+                varrho = row_ind[np.argsort(col_ind)]
+                pairs.append((i,j))
+                gw_distances.append(ged(G1, G2, cf=cf, rho=rho, varrho=varrho)[0])
+
+                ged_distances.append(ged(G1, G2, cf=cf)[0])
+
+    print("Moyenne sur Riesen : {}".format(np.mean(ged_distances)))
+    print("Moyenne sur GW : {} ".format(np.mean(gw_distances)))
+
+    np.save("distances_riesen", ged_distances)
+    np.save("distances_gw", gw_distances)

tests/requirements.txt

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+cycler==0.10.0
+Cython==0.27.3
+decorator==4.1.2
+matplotlib==2.1.0
+networkx==2.0
+numpy==1.13.3
+pkg-resources==0.0.0
+POT==0.4.0
+pyparsing==2.2.0
+python-dateutil==2.6.1
+pytz==2017.3
+scikit-learn==0.19.1
+scipy==1.0.0
+six==1.11.0
+sklearn==0.0
+tqdm==4.19.4